GR-Ben is a new process-level benchmark that evaluates error detection by PRMs and LLMs in science and logic reasoning, showing weaker performance outside mathematics.
Aurora: Automated training framework of universal process reward models via ensemble prompting and reverse verification.arXiv preprint arXiv:2502.11520
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
ATTC reduces 'Tool Ignored' errors in tool-integrated reasoning by adaptively trusting tool results according to generated code confidence, yielding 4.1-7.5% gains across models and datasets.
citing papers explorer
-
GR-Ben: A General Reasoning Benchmark for Evaluating Process Reward Models
GR-Ben is a new process-level benchmark that evaluates error detection by PRMs and LLMs in science and logic reasoning, showing weaker performance outside mathematics.
-
When to Trust Tools? Adaptive Tool Trust Calibration For Tool-Integrated Math Reasoning
ATTC reduces 'Tool Ignored' errors in tool-integrated reasoning by adaptively trusting tool results according to generated code confidence, yielding 4.1-7.5% gains across models and datasets.